A survey of the seamounts south of Tasmania on board of the Australian Marine National Facility Vessel Investigator imagery of the seafloor was collected on transects using a towed camera system. The imagery consist of Video and stereo still images. The data is structured into seamounts (Map Locations), transects (operations) and images that are geolocated in space. A selection of the stereo stills are processed to generate a quadrat of measured size overlaid on the image. The area within quadrats is annotated for (1) percent cover of substrate types with the matrix-forming coral (esp. Solenosmilia variabilis) being of particular interest, and for (2) counts of individuals of indicator taxa for ‘VME’ (Vulnerable Marine Ecosystems).
Plate 1 Example images of the habitats
Percent cover of substrate types is annotated using a point count approach in TransectMeasure software from SEAGIS. Random points at a density of 5 points.m-2) were thrown into the measured quadrat and annotated for 16 categories, including 3 matrix-forming coral species distinguished into live vs. dead, 4 other biogenic substrates, 5 non-biogenic substrate types and a not-scorable’ category where no category could be assigned due to image limitation or obstruction by mobile fauna. The CATAMI classification (Althaus et al. 2015) was used to define the substrate categories. Descriptive codes were used for each category for ease of data manipulation.In addition to the annotations of the random points an overall classification of the whole quadrat was applied to capture the overall impression, especially with regard to potential past fishing impact.
Codes and descriptions of the point categories used:
Counts of indicator taxa for vulnerable marine ecosystems (VME) are made within each quadrat, allowing for calculating standardised densities per square metre. In addition, by-eye estimated of the percent cover of coral matrix (dead & alive combined) are recorded for the three types of matrix forming coral distinguished for percent cover annotations, in order to compare percent cover from point counts and estimates. Annotations are made in the CSIRO insidence of the MBARI developed Video Annotation and Registration System (VARS). The VME taxa targetted are listed below. Again, the CATAMI classification (Althaus et al. 2015) was used to classify the taxa. The comments field in VARS is used to record counts and to record additional modifyiers such as colour of distinctive taxa within some of the VME taxon groups (e.g. yellow and brown stalked crinoids were distinguished). An additional category of ‘No-VMEfauna’ was added to indicate that an image was annotated but none of the taxa of interest were observed.
VME taxa targetted:
Additional taxa of interest:
Percent cover of matrix forming coral (dead & alive), by-eye estimate:
The TM raw output data set is a concatenation of all TM project outputs. Each of the output files consist of the point annotations within images and the ‘user defined’ point annotation for the entire image. The raw data extract is tidied up using the separate R script Tidy_TM_Concat_data.R. This separates the raw data into overview annotations and point annotations and transformed into percent cover by substrate category for each image. Then the latter are linked to image geolocation information and written out to .cvs as column-format data (PCcover.csv) and as matrix format (PCcoverbyImage.csv), for use in further analyses and for mapping in QGIS.
Note, at this stage the overview annotations were separated out from percent cover data but are not furher cleand or considered.
The VME taxon counts are extracted from the VARS Oracle data base using the script VARS_2018-StillsAnnoExtracts.sql. The raw data extract is tidied up using the separate R script VME_anno_tidy.R. The non-numrical comments are separated from the counts and percent covers; these are currently not further used. The numeric data is split into percent cover data and the counts. The latter are linked to the quadrat sizes and converted to densities (ind.m-2). each of the tables is linked to image geolocation information and written out to .cvs as column-format data (VMEanno_PCcoral.csv and VMEanno_DensQ.csv) and combined as matrix format (VMEannoMatrix.csv), for use in further analyses and for mapping in QGIS.
Note, at this stage the modifyiers (colour, etc) are not incorporated into the output data.
Inital data summaries and plots based on the by image operations details and the five .csv files written out by the data cleaning process.
# read all stills data and make depth numeric
AllSTills <- read_csv("data/IN2018_V06_AllStills.csv") %>%
mutate(depth=as.numeric(Z)) %>%
select(-c(Z))
PCcoverbyImage <- read_csv("Results/PCcoverbyImage.csv")
PC_cover <- read_csv("Results/PCcover.csv")
VMEanno_DensQ <- read_csv("Results/VMEanno_DensQ.csv")
VMEanno_PCcoral <- read_csv("Results/VMEanno_PCcoral.csv")
VMEannoMatrix <- read_csv("Results/VMEannoMatrix.csv")
SelectedStlls <- AllSTills %>%
filter(`Selection round (1 orig sel, 2 replacement)` == 1 |
`Selection round (1 orig sel, 2 replacement)` == 2 |
`Selection round (1 orig sel, 2 replacement)` == 3)
NSNR <- SelectedStlls %>%
filter(RAN_SEL_STAT == "NS-NR")
In total 5985 images were potentially targeted for annotation, of these 4987 have been annotated for percent cover of substrate types, 3288 have been annotated for VME taxa and 96 were identified as unsuitable for annotation and could not be replaced. Summary of the number of images targeted for stills analyses and the number of images annotated for percent cover and for VME taxa. (NOTE need to exclude operations that were not targeted from this summary)
PC_done <- PCcoverbyImage %>%
group_by(SVY_OPS) %>%
summarise(PCdone=n())
VME_done <- VMEannoMatrix %>%
group_by(SVY_OPS) %>%
summarise(VMEdone=n())
## NOTE will need to limit the data here to target ops only
Target <- AllSTills %>%
group_by(SVY_OPS, MapLoc, `Target RANSMPL (1/10)`) %>%
filter(`Selection round (1 orig sel, 2 replacement)` == 1 |
`Selection round (1 orig sel, 2 replacement)` == 2 |
`Selection round (1 orig sel, 2 replacement)` == 3) %>%
filter(RAN_SEL_STAT != "R") %>%
summarise(Selected = n()) %>%
left_join(PC_done, by=c("SVY_OPS"="SVY_OPS")) %>%
left_join(VME_done, by=c("SVY_OPS"="SVY_OPS"))
#tried to call this in text xxx with `r view(nTarget)` but doesn't work
kable(Target[1:6], caption="By operation processing summary")
| SVY_OPS | MapLoc | Target RANSMPL (1/10) | Selected | PCdone | VMEdone |
|---|---|---|---|---|---|
| IN2018_V06_012 | Hill U | 27 | 50 | NA | NA |
| IN2018_V06_013 | Hill U | 22 | 39 | NA | NA |
| IN2018_V06_014 | Hill U | 17 | 33 | NA | NA |
| IN2018_V06_015 | Pedra | 53 | 95 | 94 | NA |
| IN2018_V06_021 | z16 | 18 | 38 | 31 | NA |
| IN2018_V06_022 | Pedra | 21 | 51 | 50 | NA |
| IN2018_V06_024 | z16 | 41 | 41 | NA | NA |
| IN2018_V06_025 | Pedra | 63 | 63 | 50 | 33 |
| IN2018_V06_026 | Mongrel | 64 | 64 | 36 | NA |
| IN2018_V06_027 | Pedra | 95 | 97 | 95 | NA |
| IN2018_V06_029 | Mongrel | 59 | 59 | 26 | NA |
| IN2018_V06_031 | Hill U | 41 | 41 | 29 | 31 |
| IN2018_V06_032 | Hill U | 55 | 54 | 50 | 33 |
| IN2018_V06_040 | Pedra | 80 | 80 | 78 | 50 |
| IN2018_V06_041 | z16 | 50 | 50 | 48 | 30 |
| IN2018_V06_042 | Sisters | 81 | 81 | 81 | 60 |
| IN2018_V06_043 | Sisters | 83 | 83 | 81 | 50 |
| IN2018_V06_044 | Sisters | 72 | 71 | 71 | 46 |
| IN2018_V06_045 | Hill K1 | 44 | 44 | 27 | 29 |
| IN2018_V06_046 | Sisters | 67 | 67 | 63 | 43 |
| IN2018_V06_051 | Hill K1 | 80 | 81 | 43 | 42 |
| IN2018_V06_052 | Hill K1 | 48 | 48 | 23 | 24 |
| IN2018_V06_053 | Hill K1 | 57 | 57 | 26 | 26 |
| IN2018_V06_054 | Hill K1 | 48 | 48 | 19 | 19 |
| IN2018_V06_055 | Hill K1 | 60 | 60 | 36 | 36 |
| IN2018_V06_056 | Sisters | 70 | 70 | 68 | 41 |
| IN2018_V06_057 | Sisters | 67 | 67 | 61 | 33 |
| IN2018_V06_059 | Sisters | 56 | 56 | 55 | 36 |
| IN2018_V06_060 | Little Sister (z56) | 38 | 39 | 38 | 24 |
| IN2018_V06_061 | Hill U | 38 | 38 | 25 | 23 |
| IN2018_V06_062 | z16 | 39 | 39 | 39 | 25 |
| IN2018_V06_063 | z16 | 43 | 43 | 46 | 31 |
| IN2018_V06_064 | z16 | 37 | 37 | 32 | 20 |
| IN2018_V06_067 | Sisters | 63 | 63 | 63 | 23 |
| IN2018_V06_068 | Hill K1 | 45 | 46 | 15 | 6 |
| IN2018_V06_075 | Main Matt | 66 | 66 | 65 | 51 |
| IN2018_V06_080 | Fang | 25 | 25 | 24 | NA |
| IN2018_V06_081 | Sisters | 57 | 57 | 30 | 29 |
| IN2018_V06_082 | Hill K1 | 41 | 41 | 33 | 31 |
| IN2018_V06_083 | Main Matt | 83 | 83 | 83 | 67 |
| IN2018_V06_084 | Main Matt | 81 | 81 | 80 | 65 |
| IN2018_V06_085 | Main Matt | 75 | 75 | 73 | 56 |
| IN2018_V06_088 | Main Matt | 79 | 79 | 79 | 60 |
| IN2018_V06_089 | Main Matt | 69 | 69 | 69 | 57 |
| IN2018_V06_090 | slope | 36 | 36 | 36 | 34 |
| IN2018_V06_091 | Main Matt | 95 | 95 | 94 | 79 |
| IN2018_V06_092 | Main Matt | 66 | 69 | 51 | 51 |
| IN2018_V06_093 | Fang | 20 | 20 | 19 | 11 |
| IN2018_V06_095 | z99 | 66 | 67 | 66 | 47 |
| IN2018_V06_097 | Main Matt | 64 | 64 | 63 | 47 |
| IN2018_V06_098 | z110 | 74 | 74 | 70 | 37 |
| IN2018_V06_099 | slope | 76 | 76 | 66 | 42 |
| IN2018_V06_100 | slope | 59 | 59 | 59 | 26 |
| IN2018_V06_101 | slope | 66 | 66 | 66 | NA |
| IN2018_V06_103 | slope | 60 | 60 | 33 | 33 |
| IN2018_V06_104 | slope | 17 | 17 | NA | 9 |
| IN2018_V06_106 | slope | 61 | 60 | 22 | 21 |
| IN2018_V06_107 | slope | 15 | 15 | 9 | 9 |
| IN2018_V06_108 | slope | 59 | 59 | 54 | 34 |
| IN2018_V06_109 | slope | 69 | 69 | 60 | 35 |
| IN2018_V06_110 | Pedra | 54 | 54 | 31 | 27 |
| IN2018_V06_113 | z16 | 33 | 31 | 33 | 28 |
| IN2018_V06_114 | z16 | 35 | 35 | 35 | 30 |
| IN2018_V06_116 | Hill U | 83 | 83 | 45 | 49 |
| IN2018_V06_117 | Hill U | 34 | 34 | 14 | 16 |
| IN2018_V06_118 | z34 | 44 | 44 | 27 | 16 |
| IN2018_V06_119 | z20 | 24 | 24 | 11 | 9 |
| IN2018_V06_120 | slope | 51 | 51 | 20 | 20 |
| IN2018_V06_121 | Belinda’s Dory Hill | 18 | 18 | 6 | 6 |
| IN2018_V06_122 | z103 | 64 | 64 | 26 | 22 |
| IN2018_V06_123 | Hill V | 34 | 34 | 11 | 11 |
| IN2018_V06_125 | z16 | 36 | 36 | 34 | 31 |
| IN2018_V06_127 | z4 | 46 | 52 | 49 | 35 |
| IN2018_V06_128 | Punch’s Hill | 22 | 23 | 22 | 15 |
| IN2018_V06_129 | slope | 62 | 62 | 21 | 21 |
| IN2018_V06_130 | slope | 63 | 63 | 61 | 35 |
| IN2018_V06_131 | Andy’s | 33 | 33 | 13 | 7 |
| IN2018_V06_133 | slope | 63 | 63 | 34 | 34 |
| IN2018_V06_134 | slope | 69 | 69 | 28 | 16 |
| IN2018_V06_136 | slope | 70 | 70 | 69 | 17 |
| IN2018_V06_137 | Patience | 20 | 20 | NA | NA |
| IN2018_V06_138 | slope | 40 | 40 | 38 | NA |
| IN2018_V06_145 | z77 | 70 | 70 | 69 | 46 |
| IN2018_V06_146 | z5 | 72 | 72 | 71 | 28 |
| IN2018_V06_147 | New 1 | 57 | 57 | 53 | 35 |
| IN2018_V06_148 | z96 | 12 | 12 | 10 | 8 |
| IN2018_V06_150 | Atoll | 78 | 77 | 76 | 32 |
| IN2018_V06_151 | z12 | 47 | 47 | 46 | 22 |
| IN2018_V06_152 | z70 | 31 | 31 | 30 | 10 |
| IN2018_V06_154 | slope | 45 | 45 | 41 | 24 |
| IN2018_V06_155 | Punch’s Hill | 30 | 30 | 30 | 17 |
| IN2018_V06_156 | Corvina group N | 30 | 30 | 29 | 17 |
| IN2018_V06_158 | z53 | 75 | 75 | 73 | 49 |
| IN2018_V06_161 | z44 | 33 | 33 | 32 | 27 |
| IN2018_V06_162 | Patience | 51 | 51 | 46 | 37 |
| IN2018_V06_164 | z91 | 25 | 25 | 24 | 13 |
| IN2018_V06_165 | z24 | 33 | 33 | 32 | 24 |
| IN2018_V06_166 | z8 | 70 | 70 | 67 | 49 |
| IN2018_V06_167 | Punch’s Hill | 51 | 51 | 50 | 27 |
| IN2018_V06_168 | z4 | 37 | 37 | 36 | 13 |
| IN2018_V06_172 | St Helens | 79 | 79 | 76 | 76 |
| IN2018_V06_174 | St Helens | 77 | 77 | 75 | 74 |
| IN2018_V06_176 | St Helens | 66 | 66 | 60 | 59 |
| IN2018_V06_177 | St Helens | 68 | 68 | 65 | 65 |
| IN2018_V06_179 | St Helens | 70 | 70 | 70 | 69 |
| IN2018_V06_181 | St Helens (E) | 39 | 39 | 37 | NA |
| IN2018_V06_182 | St Helens | 66 | 66 | 66 | 65 |
| IN2018_V06_185 | St Helens | 71 | 71 | 71 | 71 |
| IN2018_V06_186 | St Helens | 66 | 66 | 63 | 63 |
| IN2018_V06_187 | Paddys Head | 56 | 56 | NA | NA |
| IN2018_V06_188 | Paddys Head (E) | 35 | 35 | 33 | NA |
| IN2018_V06_189 | Freycinet Canyon | 45 | 45 | 45 | NA |
| IN2018_V06_190 | Reidle Hill | 81 | 81 | 80 | 76 |
| The depth distrib | ution of the data set |
PCcoverbyImage %>%
ggplot(aes(x = depth)) +
facet_wrap(~ MapLoc) +
geom_histogram(bins=10)
Figure 4.1: depth distribution of the annotated imagery
In total there are 4987 randomly selected images have been annotated for percent cover to date. In total 106 operations have been (at least partially) annotated.
Below is a summary of the data distribution across the targeted substrate types
# check out the substrate codes that were annotated
PCsum <- PC_cover %>%
group_by(L2_Code) %>%
summarise(meanPCcover= mean(PC_cover), PresNo_Images = n())
kable(PCsum [1:3], caption="Summary of the data distribution across the targeted substrate types")
| L2_Code | meanPCcover | PresNo_Images |
|---|---|---|
| NS | 5.381557 | 110 |
| SC-ENLP | 9.975092 | 9 |
| SC-MAD | 7.459777 | 4 |
| SC-SOL | 13.877160 | 327 |
| SU-BBAR | 22.897745 | 164 |
| SU-BCOR | 34.058841 | 1216 |
| SU-BOL | 12.377628 | 175 |
| SU-BOTH | 28.104067 | 1136 |
| SU-COB | 9.133321 | 104 |
| SU-CONBIO | 29.371541 | 1710 |
| SU-ENLP | 12.801383 | 9 |
| SU-MAD | 4.918033 | 1 |
| SU-PEBGRAV | 58.894995 | 1189 |
| SU-ROK | 73.650442 | 2208 |
| SU-SAMU | 40.812051 | 1771 |
| SU-SOL | 60.720625 | 941 |
| NA | 48.639209 | 15 |
The depth distribution of the live and dead coral matrix are of particular interest in looking at the depth distribution of the substrate types.
#distribution of the substrate types
# create a vector with the sequence of the substrate types for ordering them in a meaningful way
SubstSeq <- c('SC-ENLP',
'SU-ENLP',
'SC-SOL',
'SU-SOL',
'SC-MAD',
'SU-MAD',
'SU-BCOR',
'SU-BBAR',
'SU-BOTH',
'SU-ROK',
'SU-BOL',
'SU-COB',
'SU-CONBIO',
'SU-PEBGRAV',
'SU-SAMU',
'NS')
ggplot(PC_cover,
mapping= aes(x=factor(L2_Code, level =SubstSeq), #call the pre existing vector
y=depth,
size=PC_cover)
)+
geom_point(alpha=0.2)+
scale_y_reverse() + # reverse y-axis because it represents ocean depth
theme(axis.text.x = element_text(angle = 90))+ # rotate the label on x-axis
labs(x="substrate type", y="depth")
Figure 4.2: Depth distribution of substrate types summary graph
Coral rubble appears to have quite a high concentratio in the shallowest depths, scattering the raw data may make this easier to see:
PC_cover %>%
ggplot(aes(x = depth,
y = PC_cover))+
geom_point(alpha=0.2)+
facet_wrap(~L2_Code)
(#fig:FigPC_DpthDist2)Depth distribution of substrate types
Distribution of the substrate types by selected locations - here the seamounts from the recovery time series are selected.
# currentl the pie graphs are not run for the knittin...
Fang <- PC_cover %>%
filter(MapLoc=="Fang") %>%
ggplot(Fang,
mapping= aes(x="",
y=PC_cover,
fill=factor(L2_Code, level =SubstSeq)
))+
geom_bar(stat="identity", width=1)+
ggtitle("Fang")+
coord_polar("y", start=0)
Fang
MainMatt <- PC_cover %>%
filter(MapLoc=="Main Matt") %>%
ggplot(MainMatt,
mapping= aes(x="",
y=PC_cover,
fill=factor(L2_Code, level =SubstSeq)
))+
geom_bar(stat="identity", width=1)+
ggtitle("MainMatt")+
coord_polar("y", start=0)
MainMatt
Pedra <- PC_cover %>%
filter(MapLoc=="Pedra") %>%
ggplot(Pedra,
mapping= aes(x="",
y=PC_cover,
fill=factor(L2_Code, level =SubstSeq)
))+
geom_bar(stat="identity", width=1)+
ggtitle("Pedra")+
coord_polar("y", start=0)
Pedra
Sisters <- PC_cover %>%
filter(MapLoc=="Sisters") %>%
ggplot(MainMatt,
mapping= aes(x="",
y=PC_cover,
fill=factor(L2_Code, level =SubstSeq)
))+
geom_bar(stat="identity", width=1)+
ggtitle("Sisters")+
coord_polar("y", start=0)
Sisters
z16 <- PC_cover %>%
filter(MapLoc=="z16") %>%
ggplot(z16,
mapping= aes(x="",
y=PC_cover,
fill=factor(L2_Code, level =SubstSeq)
))+
geom_bar(stat="identity", width=1)+
ggtitle("z16")+
coord_polar("y", start=0)
z16
Hill_U <- PC_cover %>%
filter(MapLoc=="Hill U") %>%
ggplot(Hill_U,
mapping= aes(x="",
y=PC_cover,
fill=factor(L2_Code, level =SubstSeq)
))+
geom_bar(stat="identity", width=1)+
ggtitle("Hill_U")+
coord_polar("y", start=0)
Hill_U
Hill_K1 <- PC_cover %>%
filter(MapLoc=="Hill K1") %>%
ggplot(Hill_U,
mapping= aes(x="",
y=PC_cover,
fill=factor(L2_Code, level =SubstSeq)
))+
geom_bar(stat="identity", width=1)+
ggtitle("Hill_K1")+
coord_polar("y", start=0)
Hill_K1
Figure 2 above shows a relatively narrow depth band where Solenosmilia (particulaly live coral) is observed, and a surprisingly high contribution of coral rubble in the shallowest images. Looking at the distribution of coral matrix formed by Solenosmilia (SC_SOL and SU_SOL) and coral rubble (SU_BCOR) in more detail, particularly by depth and location.
CoralPC <- PC_cover %>%
filter(L2_Code == "SC-SOL" |
L2_Code == "SU-SOL" |
L2_Code == "SU-BCOR" )
CoralPC %>%
ggplot(aes(x = PC_cover)) +
facet_wrap(~ L2_Code) +
geom_histogram()
(#fig:FigSV_histogram)Frequency distrbution histograms of % cover recorded for coral matrix and rubble
Just concentrating on the Coral rubble, why is there a high proportion of rubble in shallow sites
PC_cover %>%
filter(L2_Code == "SU-BCOR") %>%
ggplot(aes(x = depth,
y = PC_cover))+
geom_point(alpha=0.2)+
facet_wrap(~MapLoc)
(#fig:FigRubble_checks)Distribution of coral rubble by seamount location
In total there are 3288 randomly selected images have been annotated for the density of VME taxa to date. In total 96 operations have been (at least partially) annotated.
# looking at the distribution of density and number of taxa over the whole data set
VME_TotDens <- VMEanno_DensQ %>%
filter(CONCEPT != "No-VMEfauna") %>%
group_by(image_key, SVY_OPS, MapLoc, depth) %>%
summarise(TotDens=sum(Dens),
noTaxa=sum(NoTypes))
TotVME_dens <- VME_TotDens %>%
ggplot(aes(x=TotDens))+
geom_histogram()
# number of Taxa
TotVMEtax <- VME_TotDens %>%
ggplot(aes(x=noTaxa))+
geom_histogram()
# combine plots into a 2 panel figure
plot_grid(TotVMEtax, TotVME_dens)
(#fig:FigPCcover_explore)*Frequency distribution of a) total density of VME data and b) total number of taxa
Instead of total density only use the totals for actual VME taxa (excluding urchins,)
# looking at the distribution of density and number of taxa over the whole data set
VMEonly_TotDens <- VMEanno_DensQ %>%
filter( CONCEPT == "Black & Octocorals" |
CONCEPT == "Brisingid" |
#CONCEPT == "D.horridus",
CONCEPT == "Enallopsammia" |
#CONCEPT == "Hydrocorals" |
CONCEPT == "Hydrocorals: Branching" |
#CONCEPT == "Irregular urchins" |
CONCEPT == "Madrepora" |
#CONCEPT == "No-VMEfauna",
#CONCEPT == "Regular urchins" |
CONCEPT == "S.variabilis" |
CONCEPT == "Sponges" |
CONCEPT == "Stalked crinoids" |
CONCEPT == "Stony corals",
#CONCEPT == "True anemones: Fourlobed" |
#CONCEPT == "Unstalked crinoids"
)%>%
group_by(image_key, SVY_OPS, MapLoc, depth) %>%
summarise(VMEtaxaDens=sum(Dens))
VMEonly_TotDens %>%
ggplot(aes(x = VMEtaxaDens))+
geom_histogram()
(#fig:FigDensity_explore)**Frequency distribution of total density of VME taxa only
Combining the totals of the VME taxa annotations with the percent cover annotations allows us to examine correlations between Total VME fauna density and substrate types.
Checking how well the estimated percent cover of coral matrix matches with measured percent cover of coral matrix (dead & alive)
VME_AnnoAll %>%
ggplot(mapping = aes(x= (`SC-SOL`+`SU-SOL`),
y= PC_SolMatrix))+
geom_point()
Figure 4.3: Comparison of estimated and measured percent cover of matrix
estVmeas_lm <- lm(PC_SolMatrix ~ (`SC-SOL`+`SU-SOL`), data = VME_AnnoAll)
estVmeas_lm
##
## Call:
## lm(formula = PC_SolMatrix ~ (`SC-SOL` + `SU-SOL`), data = VME_AnnoAll)
##
## Coefficients:
## (Intercept) `SC-SOL` `SU-SOL`
## -0.1761 1.9940 0.6574
Looking into the overview scores of potential gear impacts for still images
# data exploration for overview scoring
OvCat<- PCcoverbyImage %>%
group_by(OV_CAT) %>%
summarise(cntRec = n())
kable(OvCat, caption="overview categories scored")
| OV_CAT | cntRec |
|---|---|
| High - reef | 330 |
| High - shaved | 158 |
| HighAb - no other | 39 |
| HighAb - with clumps - dead | 28 |
| HighAb - with clumps - live | 2 |
| HighAb - with other | 303 |
| HighAb - with patch - dead | 15 |
| HighAb - with patch - live | 13 |
| Isolated fragments/ clumps | 197 |
| Low - sediment filled | 155 |
| Low - shaved | 156 |
| Low - with clumps - dead | 12 |
| Low - with clumps - live | 2 |
| Low - with patch - dead | 28 |
| Low - with patch - live | 64 |
| LowAb - no clumps | 97 |
| LowAb - with clumps | 29 |
| MedAb | 481 |
| MedAb - with clumps - dead | 270 |
| MedAb - with clumps - live | 9 |
| MedAb - with patch - dead | 64 |
| MedAb - with patch - live | 37 |
| No rubble or matrix | 2430 |
| Shaved | 41 |
| NA | 27 |
PCcoverbyImage %>%
ggplot(aes(x = depth,
y = `SU-BCOR`,
colour = OV_CAT))+
geom_point(alpha=0.2)+
facet_wrap(~MapLoc)
Figure 4.4: Impact overview evaluation
PCcoverbyImage %>%
ggplot(aes(x = depth,
y = (`SU-SOL`+ `SC-SOL`),
colour = OV_CAT))+
geom_point(alpha=0.2)+
facet_wrap(~MapLoc)
Figure 4.5: Impact overview evaluation
PC_cover %>%
group_by(L2_Code) %>%
ggplot(aes(x = `OV_CAT`,
y = mean(PC_cover),
colour = L2_Code)) +
geom_col() +
theme(axis.text.x.bottom = element_text(angle = 90))
Figure 4.6: Impact overview evaluation
Looking at the distribution of the overview categories by seamount will help to identify locations for further scrutiny:
## PC_cover %>%
# filter(
# OV_CAT != `High - reef` &
# OV_CAT != `Low - sediment filled`) %>%
#group_by(MapLoc, OV_CAT) %>%
# ggplot(aes(x = MapLoc,
# y = mean(PC_cover),
# colour = OV_CAT)) +
# geom_col() +
# theme(axis.text.x.bottom = element_text(angle = 90))